1. Technical Field
The present disclosure relates to machine condition monitoring and, more specifically, to incremental learning of nonlinear regression networks for machine condition monitoring.
2. Discussion of the Related Art
Condition monitoring relates to the observation and analysis of one or more sensors that sense key parameters of machinery. By closely observing the sensor data, a potential failure or inefficiency may be detected and remedial action may be taken, often before a major system failure occurs.
Effective condition monitoring may allow for increased uptime, reduced costs associated with failures, and a decreased need for prophylactic replacement of machine components.
Condition monitoring may be applied to a wide variety of industrial machinery such as capitol equipment, factories and power plants; however, condition monitoring may also be applied to other mechanical equipment such as automobiles and nonmechanical equipment such as computers. In fact, principals of condition monitoring may be applied more generally to any system or organization. For example, principals of condition monitoring may be used to monitor the vital signs of a patient to detect potential health problems. For example, principals of condition monitoring may be applied to monitor performance and/or economic indicators to detect potential problems with a corporation or an economy.
In condition monitoring, one or more sensors may be used. Examples of commonly used sensors include vibration sensors for analyzing a level of vibration and/or the frequency spectrum of vibration. Other examples of sensors include temperature sensors, pressure sensors, spectrographic oil analysis, ultrasound, and image recognition devices.
A sensor may be a physical sensory device that may be mounted on or near a monitored machine component or a sensor may more generally refer to a source of data.
Conventional techniques for condition monitoring acquire data from the one or more sensors and analyze the collected data to detect when the data is indicative of a potential fault. Inferential sensing is an example of an approach that may be used to determine when sensor data is indicative of a potential fault.
In inferential sensing, an expected value for a particular sensor is estimated, for example, through the use of other sensors, and an actual sensor value is observed. The actual sensor value may then be compared to the expected sensor value, and the larger the difference between the two values, the greater the likelihood of a potential fault.
As calculating the expected value for a particular sensor may involve a large number of inputs, a regression network may be used to return one output for M number of inputs. A simple example of a regression network is a linear regression model. In such cases, the expected value is calculated based on a linear relationship of the M inputs. However, in practical use, a linear regression model may be insufficient to properly represent the relationship between the M inputs and the estimated expected value.